{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## License Information\n",
    "---\n",
    "\n",
    "This file is distributed as part of `hvsrpy`, a Python package for horizontal-to-vertical spectral ratio processing.\n",
    "\n",
    "    Copyright (C) 2019-2021 Joseph P. Vantassel (jvantassel@utexas.edu)\n",
    "\n",
    "    This program is free software: you can redistribute it and/or modify\n",
    "    it under the terms of the GNU General Public License as published by\n",
    "    the Free Software Foundation, either version 3 of the License, or\n",
    "    (at your option) any later version.\n",
    "\n",
    "    This program is distributed in the hope that it will be useful,\n",
    "    but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
    "    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n",
    "    GNU General Public License for more details.\n",
    "\n",
    "    You should have received a copy of the GNU General Public License\n",
    "    along with this program.  If not, see <https: //www.gnu.org/licenses/>.\n",
    "    \n",
    "## About _hvsrpy_ and this notebook\n",
    "---\n",
    "\n",
    "`hvsrpy` is a Python package for performing horizontal-to-vertical spectral ratio\n",
    "(HVSR) processing. `hvsrpy` was developed by Joseph P. Vantassel  with\n",
    "contributions from Dana M. Brannon under the supervision\n",
    "of Professor Brady R. Cox at The University of Texas at Austin.\n",
    "\n",
    "If you use `hvsrpy` in your research or consulting, we ask you please cite the following:\n",
    "\n",
    ">Joseph Vantassel. (2020). jpvantassel/hvsrpy: latest (Concept). Zenodo.\n",
    "[http://doi.org/10.5281/zenodo.3666956](http://doi.org/10.5281/zenodo.3666956)\n",
    "\n",
    "_Note: For software, version specific citations should be preferred to\n",
    "general concept citations, such as that listed above. To generate a version\n",
    "specific citation for `hvsrpy`, please use the citation tool on the `hvsrpy`\n",
    "[archive](http://doi.org/10.5281/zenodo.3666956)._\n",
    "\n",
    "The calculations to consider spatial variability included in `hvsrpy` and\n",
    "showcased in this notebook were developed by Tianjian Cheng under the supervision of\n",
    "Professor Brady R. Cox at The University of Texas at Austin and are detailed in\n",
    "Cheng et al (2021). To recognize their original work please cite the follwing:\n",
    "\n",
    "> Cheng, T., Hallal, M. M., Vantassel, J. P., and  Cox, B. R. (2021). \"Estimating Unbiased Statistics for Fundamental Site Frequency Using Spatially Distributed HVSR Measurements and Voronoi Tessellation.\" Submitted to Journal of Geotechnical and Geoenvironmental Engineering.\n",
    "\n",
    "\n",
    "## Getting Started\n",
    "---\n",
    "1. Install _hvsrpy_ and its dependencies, with `pip install hvsrpy`. If you are not familiar with `pip`, a useful tutorial can be found [here](https://jpvantassel.github.io/python3-course/#/intro/pip). __(~3 minutes)__\n",
    "2. Run the provided example for Warehouse 2 from CentrePort, NZ. Compare the results with those from Cheng et al. (2021). __(~5 minutes)__\n",
    "3. Swap the provided .csv files (sensor information and boundary information) and repeat to develop spatial statistics for your site. __(~20 minutes)__\n",
    "\n",
    "Happy Processing!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "from matplotlib import cm\n",
    "\n",
    "from hvsrpy import plottools\n",
    "import hvsrpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Sensor Information\n",
    "---\n",
    "\n",
    "At a minimum the sensor information must include the relative x and y coordinates and an estimate of f0. It is strongly encouraged that an uncertainty estimate of f0 is also provided."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>mean</th>\n",
       "      <th>stddev</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
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       "      <td>-0.294558</td>\n",
       "      <td>0.156553</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>457.749280</td>\n",
       "      <td>314.640046</td>\n",
       "      <td>-0.417214</td>\n",
       "      <td>0.222720</td>\n",
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      "text/plain": [
       "            x           y      mean    stddev\n",
       "0  544.594606  432.890014 -0.411170  0.206193\n",
       "1  587.352069  385.720914 -0.323445  0.163446\n",
       "2  605.466471  300.679092 -0.236087  0.063194\n",
       "3  521.008642  289.127687 -0.294558  0.156553\n",
       "4  457.749280  314.640046 -0.417214  0.222720"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# File which includes hvsr statistical data.\n",
    "fname = \"sp_centreport.csv\"\n",
    "# Assumed distribution of the generators. \"lognormal\" is recommended.\n",
    "dist_generators = \"lognormal\"\n",
    "# Assumed/desired spatial distribution. \"lognormal\" is recommended.\n",
    "dist_spatial = \"lognormal\"\n",
    "\n",
    "df = pd.read_csv(fname)\n",
    "coordinates = df.to_numpy(copy=True)[:, 0:2]\n",
    "means = df.to_numpy(copy=True)[:, 2]\n",
    "stddevs = df.to_numpy(copy=True)[:, 3]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create HvsrVault Object\n",
    "---\n",
    "The `HvsrVault` object stores all the Hvsr statistical data and their coordinates. Using an object allows different boundaries to be attempted on a single dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HvsrVault object created successfully!\n"
     ]
    }
   ],
   "source": [
    "vlt = hvsrpy.HvsrVault(coordinates, means, stddevs=stddevs)\n",
    "print(\"HvsrVault object created successfully!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Boundary Information\n",
    "\n",
    "---\n",
    "Specify unique x and y coordinates to define the Voronoi tesselations boundary.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2</th>\n",
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       "      <td>569.713082</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>372.155994</td>\n",
       "      <td>542.293165</td>\n",
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       "            x           y\n",
       "0  323.402906  712.327227\n",
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"sp_w2.csv\")\n",
    "\n",
    "boundary = df.to_numpy(copy=True)[:, 0:2]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply Boundary\n",
    "---\n",
    "\n",
    "Apply boundary to `HvsrVault` and request spatial weights for declustered statistical analysis.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Sensors Inside Boundary : 8\n"
     ]
    }
   ],
   "source": [
    "weights, valid_indices = vlt.spatial_weights(boundary, dc_method=\"voronoi\")\n",
    "valid_coordinates = vlt.coordinates[valid_indices,:]\n",
    "vertices, indices = vlt.bounded_voronoi(boundary)\n",
    "\n",
    "print(f\"Number of Sensors Inside Boundary : {len(valid_indices)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Result\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics considering spatial variability:\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>Lognormal Median</th>\n",
       "      <th>Lognormal Standard Deviation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fundamental Site Frequency, f0</th>\n",
       "      <td>0.58</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fundamental Site Period, T0</th>\n",
       "      <td>1.74</td>\n",
       "      <td>0.16</td>\n",
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       "                                Lognormal Median  Lognormal Standard Deviation\n",
       "Fundamental Site Frequency, f0              0.58                          0.16\n",
       "Fundamental Site Period, T0                 1.74                          0.16"
      ]
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\n",
      "text/plain": [
       "<Figure size 630x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f0_mean, f0_stddev, f0s_realizations = hvsrpy.montecarlo_f0(means[valid_indices], stddevs[valid_indices], weights, nrealizations=1000, dist_generators=dist_generators, dist_spatial=dist_spatial)\n",
    "\n",
    "if dist_spatial == \"lognormal\":    \n",
    "    data = np.array([[f0_mean, f0_stddev],\n",
    "                     [1/f0_mean, f0_stddev]])\n",
    "    columns = [\"Lognormal Median\", \"Lognormal Standard Deviation\"]\n",
    "elif dist_spatial == \"normal\":\n",
    "    data = np.array([[f0_mean, f0_stddev],\n",
    "                     [np.nan, np.nan]])\n",
    "    columns = [\"Mean\", \"Standard Deviation\"]\n",
    "else:\n",
    "    raise ValueError(f\"dist_spatial = {dist_spatial} not recognized.\")\n",
    "\n",
    "df = pd.DataFrame(data=data, columns=columns,\n",
    "                       index=[\"Fundamental Site Frequency, f0\",\n",
    "                              \"Fundamental Site Period, T0\"])\n",
    "print(\"Statistics considering spatial variability:\")\n",
    "display(df.round(2))\n",
    "\n",
    "fig, axs = plt.subplots(ncols=2, figsize=(4.2,4), dpi=150, gridspec_kw=dict(width_ratios=(4,0.2)))\n",
    "ax = axs[0]\n",
    "ax.plot(valid_coordinates[:,0], valid_coordinates[:,1], color=\"#3399ff\", linestyle=\"\", marker=\"o\", label=\"Point Inside Boundary\", markeredgecolor=\"k\")\n",
    "\n",
    "closed_boundary = np.vstack((boundary, boundary[0,:]))\n",
    "ax.plot(closed_boundary[:,0], closed_boundary[:,1], color=\"#000000\", label=\"Boundary\",  linewidth=3)\n",
    "\n",
    "_data = means[valid_indices]\n",
    "if dist_spatial == \"lognormal\":\n",
    "    _data = np.exp(_data)\n",
    "_min, _max = np.min(_data), np.max(_data)\n",
    "norm = cm.colors.Normalize(vmin=_min, vmax=_max)\n",
    "cmap = cm.autumn\n",
    "mpl.colorbar.ColorbarBase(axs[1], cmap=cmap, norm=norm, label=\"Fundamental Frequency (Hz)\")\n",
    "\n",
    "for _dat, vertex in zip(_data, vertices):\n",
    "    ax.fill(vertex[:, 0], vertex[:, 1], facecolor=mpl.colors.rgb2hex(cmap(norm(_dat))[:3]), edgecolor=\"k\",   linewidth=0.5)\n",
    "\n",
    "ax.set_xlabel(\"Relative Easting (m)\")\n",
    "ax.set_ylabel(\"Relative Northing (m)\")\n",
    "ax.legend(loc=\"upper center\", bbox_to_anchor=(0.5, -0.15), ncol=3)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save Figure to File\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure saved successfully!\n"
     ]
    }
   ],
   "source": [
    "figure_name_out = \"example_hvsr_spatial.png\"\n",
    "\n",
    "fig.savefig(figure_name_out, dpi=300, bbox_inches='tight')\n",
    "plt.close()\n",
    "print(\"Figure saved successfully!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
